We report competitive results on object detection and instance segmentation
on the COCO dataset using standard models trained from random initialization.
The results are no worse than their ImageNet pre-training counterparts even
when using the hyper-parameters of the baseline system (Mask R-CNN) that were
optimized for fine-tuning pre-trained models, with the sole exception of
increasing the number of training iterations so the randomly initialized models
may converge. Training from random initialization is surprisingly robust; our
results hold even when: (i) using only 10% of the training data, (ii) for
deeper and wider models, and (iii) for multiple tasks and metrics. Experiments
show that ImageNet pre-training speeds up convergence early in training, but
does not necessarily provide regularization or improve final target task
accuracy. To push the envelope we demonstrate 50.9 AP on COCO object detection
without using any external data---a result on par with the top COCO 2017
competition results that used ImageNet pre-training. These observations
challenge the conventional wisdom of ImageNet pre-training for dependent tasks
and we expect these discoveries will encourage people to rethink the current de
facto paradigm of `pre-training and fine-tuning' in computer vision.

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